Image-to-Image Translation with Conditional Adversarial Networks - summary

Author: Nikolay Chehlarov date: 04.02.2022

paper: Image-to-Image Translation with Conditional Adversarial Networks

authors: Phillip Isola Jun-Yan Zhu Tinghui Zhou Alexei A. Efros Berkeley AI Research (BAIR) Laboratory, UC Berkeley

Link to paper: https://arxiv.org/abs/1611.07004
Official implementation: https://github.com/phillipi/pix2pix.

Abstract

The aim of the model is translate one to another image. Given input (picture) in one modality, output in different modality is generated. Training is done on pairs of pictures in two different modalities. Example pairs include satellite image - Google maps image, day - night, sketch - bag, semantic segment mask - real image and many more. One can provide a semantic segmented mask and obtain a realistic picture for example.

An on-line implementation can be used https://affinelayer.com/pixsrv/ to test the approach.

Traditionally, this tasks has been tackled with separate, special-purpose machinery, despite the fact that the setting is always the same: predict pixels from pixels. The goal in this paper is to develop a common framework for all these problems.

Highlights

• conditional GAN is used
• structured loss is learned during training
• suitable for wide variety of problems; the framework is not application specific
• U-net generator
• PatchGAN discriminator
• noise provided only in the form of dropout in generator at both training and test time.

Model architecture

The pix2pix is based on conditional GAN architecture. The main building block of the pix2pix architecture are:

• Generator: task - generate realistic looking pictures that fool the discriminator; In the case of pix2pix the generator is U-net (https://arxiv.org/abs/1505.04597)
• Discriminator: task - figure out if the image real or fake. The discriminator is convolutional patch GAN classifier, which only penalizes structure at the scale of image patches. It classifies patch by patch, finally the results are averaged.

Unlike typical cGAN, the generator accepts combination of two losses - one from the discriminator (GAN loss, binary cross entropy) and one for the difference between the generated output to the input pair (the mask). The second loss is mean absolute error, and is referred as L1 loss. In the paper the combined loss is weighted sum, with L1 having weight of 100. Unlike an unconditional GAN, both the generator and discriminator observe the input edge map.

The objective of a conditional GAN can be expressed as $$ \mathscr{L}_{cGAN}(G, D)=E_{x, y}[logD(x, y))] + E_{x, y}[log(1 - D(x, G(x, z)))]$$ where G tries to minimize this objective against an adversarial D that tries to maximize it.

The conditional GAN the loss is learned, and can, in theory, penalize any possible structure that differs between output and target. Conditional GANs learn a mapping from observed image xr and random noise vector z, to y.

Key details from the paper

• Use of adam solver with a learning rate of 0.0002, and momentum parameters ß1 = 0.5, ß2 = 0.999
• batch size of 1 "...when the batch size is set to 1, has been termed “instance normalization” and has been demonstrated to be effective at image generation task"
• "70x70 PatchGAN forces outputs that are sharp"
• total loss = 100 x L1 + cGAN; L1 is mean absolute error and cGAN loss is binary cross entropy loss
• all activations are leaky RelU with slope 0.2 in the encoder; the decoder ReLUs are not leaky
• Unlike typical approach, dropout is applied during training and testing
• batch normalization is applied using statistics on the test batch, rather than aggregated statistics on the training batch.
• two metrics are used for model evaluation - AMT perceptual studies and FCN-score
• the tests performed on photo to map task fooled 6.1% of the participants in the AMT test. The poor performance of the generator may be because minor structural errors are more visible on map images.

Implementation

The implementation is originated from https://github.com/bnsreenu/python_for_microscopists/tree/master/251_satellite_image_to_maps_translation with minimal modifications. This source is based on the code by Jason Brownlee from his blogs on https://machinelearningmastery.com/.

The model is targeted to create maps from satellite images.

• source image is the satellite image
• target image is the maps image

Generator:
Input - source image; Output - target image

The encoder-decoder architecture consists of:

encoder: C64-C128-C256-C512-C512-C512-C512-C512

decoder: CD512-CD512-CD512-C512-C256-C128-C64

Discriminator:

Input - pair of source and target image; output - probability between 0 and 1 that the inputs are actually a pair

C64-C128-C256-C512

After the last layer, a convolution is applied to map to a 1-dimensional output, followed by a Sigmoid function.

from matplotlib import pyplot as plt
from os import listdir
from datetime import datetime 
import numpy as np
from numpy import asarray, load, vstack, savez_compressed, zeros, ones
from numpy.random import randint

from tensorflow.keras.optimizers import Adam
from tensorflow.keras.initializers import RandomNormal
from tensorflow.keras.models import Model, load_model
from tensorflow.keras.layers import Input, Conv2D, Conv2DTranspose, LeakyReLU, Activation, Concatenate, Dropout, BatchNormalization
from tensorflow.keras.utils import plot_model
from tensorflow.keras.preprocessing.image import img_to_array, load_img

Function definition

#############################################################################
#Define generator, discriminator, gan and other helper functions
#We will use functional way of defining model and not sequential
#as we have multiple inputs; both images and corresponding labels. 
########################################################################


def define_discriminator(image_shape):
    
    # weight initialization
    init = RandomNormal(stddev=0.02) #As described in the original paper
    
    # source image input
    in_src_image = Input(shape=image_shape)  #Image we want to convert to another image
    # target image input
    in_target_image = Input(shape=image_shape)  #Image we want to generate after training. 
    
    # concatenate images, channel-wise
    merged = Concatenate()([in_src_image, in_target_image])
    
    # C64: 4x4 kernel Stride 2x2
    d = Conv2D(64, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(merged)
    d = LeakyReLU(alpha=0.2)(d)
    # C128: 4x4 kernel Stride 2x2
    d = Conv2D(128, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d)
    d = BatchNormalization()(d)
    d = LeakyReLU(alpha=0.2)(d)
    # C256: 4x4 kernel Stride 2x2
    d = Conv2D(256, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d)
    d = BatchNormalization()(d)
    d = LeakyReLU(alpha=0.2)(d)
    # C512: 4x4 kernel Stride 2x2 
    # This block is not in the original paper. 
    # d = Conv2D(512, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d)
    # d = BatchNormalization()(d)
    # d = LeakyReLU(alpha=0.2)(d)
    # second last output layer : 4x4 kernel but Stride 1x1
    d = Conv2D(512, (4,4), padding='same', kernel_initializer=init)(d)
    d = BatchNormalization()(d)
    d = LeakyReLU(alpha=0.2)(d)
    # patch output
    d = Conv2D(1, (4,4), padding='same', kernel_initializer=init)(d)
    patch_out = Activation('sigmoid')(d)
    # define model
    model = Model([in_src_image, in_target_image], patch_out)
    # compile model
    #The model is trained with a batch size of one image and Adam opt. 
    #with a small learning rate and 0.5 beta1. 
    #The loss for the discriminator is weighted by 50% for each model update.
    
    opt = Adam(learning_rate=0.0002, beta_1=0.5)
    model.compile(loss='binary_crossentropy', optimizer=opt, loss_weights=[0.5])
    
    return model

# disc_model = define_discriminator((256,256,3))
# disc_model.summary()

def define_encoder_block(layer_in, n_filters, batchnorm=True):
    # define an encoder block to be used in generator
    init = RandomNormal(stddev=0.02)
    g = Conv2D(n_filters, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(layer_in)
    if batchnorm:
        g = BatchNormalization()(g, training=True)
    g = LeakyReLU(alpha=0.2)(g)
    return g


def decoder_block(layer_in, skip_in, n_filters, dropout=True):
    # define a decoder block to be used in generator
    init = RandomNormal(stddev=0.02)
    g = Conv2DTranspose(n_filters, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(layer_in)
    g = BatchNormalization()(g, training=True)
    if dropout:
        g = Dropout(0.5)(g, training=True)
    # merge with skip connection
    g = Concatenate()([g, skip_in])
    g = Activation('relu')(g)
    return g


def define_generator(image_shape=(256,256,3)):
    """
    Definint the generator, in this case U-net
    Inputs should be in the range -1 to 1; generated outputs are also in this range
    """
    init = RandomNormal(stddev=0.02)
    in_image = Input(shape=image_shape)
    
    # encoder model: C64-C128-C256-C512-C512-C512-C512-C512
    e1 = define_encoder_block(in_image, 64, batchnorm=False)
    e2 = define_encoder_block(e1, 128)
    e3 = define_encoder_block(e2, 256)
    e4 = define_encoder_block(e3, 512)
    e5 = define_encoder_block(e4, 512)
    e6 = define_encoder_block(e5, 512)
    e7 = define_encoder_block(e6, 512)
    
    # bottleneck, no batch norm 
    b = Conv2D(512, (4,4), strides=(2,2), padding='same', kernel_initializer=init)(e7)
    b = Activation('relu')(b)
    
    # decoder model: CD512-CD512-CD512-C512-C256-C128-C64
    d1 = decoder_block(b, e7, 512)
    d2 = decoder_block(d1, e6, 512)
    d3 = decoder_block(d2, e5, 512)
    d4 = decoder_block(d3, e4, 512, dropout=False)
    d5 = decoder_block(d4, e3, 256, dropout=False)
    d6 = decoder_block(d5, e2, 128, dropout=False)
    d7 = decoder_block(d6, e1, 64, dropout=False)
    # output
    g = Conv2DTranspose(image_shape[2], (4,4), strides=(2,2), padding='same', kernel_initializer=init)(d7) 
    out_image = Activation('tanh')(g)  #Generates images in the range -1 to 1.
    
    # define model, without compile
    model = Model(in_image, out_image)
    return model

# gen_model = define_generator((256,256,3))
# gen_model.summary()

def define_gan(g_model, d_model, image_shape):
    # define the combined generator and discriminator model, for updating the generator
    # make weights in the discriminator not trainable
    for layer in d_model.layers:
        if not isinstance(layer, BatchNormalization):
            layer.trainable = False       #Descriminator layers set to untrainable in the combined GAN but 
                                                #standalone descriminator will be trainable.
            
    # define the source image
    in_src = Input(shape=image_shape)
    # suppy the image as input to the generator 
    gen_out = g_model(in_src)
    # supply the input image and generated image as inputs to the discriminator
    dis_out = d_model([in_src, gen_out])
    # src image as input, generated image and disc. output as outputs
    model = Model(in_src, [dis_out, gen_out])
    
    opt = Adam(learning_rate=0.0002, beta_1=0.5)
    
    #Total loss is the weighted sum of adversarial loss (BCE) and L1 loss (MAE)
    #Authors suggested weighting BCE vs L1 as 1:100.
    model.compile(loss=['binary_crossentropy', 'mae'], 
                  optimizer=opt,
                  loss_weights=[1, 100]
                 )
    return model


def generate_real_samples(dataset, n_samples, patch_shape, pic_idx=None):
    # select a batch of random samples, returns images and target
    # unpack dataset
    trainA, trainB = dataset # trainA is satellite image, trainB is coresponding maps image
    if pic_idx == None:
        # choose random instances
        ix = randint(0, trainA.shape[0], n_samples)
    else:
        ix = np.array(pic_idx)
    # retrieve selected images
    X1, X2 = trainA[ix], trainB[ix]
    # generate 'real' class labels (1)
    y = ones((n_samples, patch_shape, patch_shape, 1))
    return [X1, X2], y


def generate_fake_samples(g_model, samples, patch_shape):
    # generate a batch of images, returns images and targets
    # generate fake instance
    X = g_model.predict(samples)
    # create 'fake' class labels (0)
    y = zeros((len(X), patch_shape, patch_shape, 1))
    return X, y


def summarize_performance(step, g_model, data_set, n_samples=3, pic_idx=None):
    """
    step : a strin gindicating the step of the trainning
    generate samples and save as a plot and save the model
    GAN models do not converge, we just want to find a good balance between
    the generator and the discriminator. Therefore, it makes sense to periodically
    save the generator model and check how good the generated image looks. 
    """
    # select a sample of input images
    [X_realA, X_realB], _ = generate_real_samples(data_set, n_samples, 1, pic_idx=pic_idx)
    # generate a batch of fake samples
    X_fakeB, _ = generate_fake_samples(g_model, X_realA, 1)
    
    #     print("before")
    #     print(f"data_set max {data_set[0].max()}, min {data_set[0].min()}")
    #     print(f"shape {X_realA.shape}; min/max {X_realA.min()}/{X_realA.max()}")
    #     print(f"shape {X_realB.shape}; min/max {X_realB.min()}/{X_realB.max()}")
    #     print(f"shape {X_fakeB.shape}; min/max {X_fakeB.min()}/{X_fakeB.max()}")
    # scale all pixels from [-1,1] to [0,1]
    X_realA = (X_realA + 1) / 2.0
    X_realB = (X_realB + 1) / 2.0
    X_fakeB = (X_fakeB + 1) / 2.0
        
    # plot real source images
    fig = plt.figure(figsize=(10,10))
    for i in range(n_samples):
        plt.subplot(3, n_samples, 1 + i)
        plt.axis('off')
        plt.imshow(X_realA[i])
    # plot generated target image
    for i in range(n_samples):
        plt.subplot(3, n_samples, 1 + n_samples + i)
        plt.axis('off')
        plt.imshow(X_fakeB[i])
    # plot real target image
    for i in range(n_samples):
        plt.subplot(3, n_samples, 1 + n_samples*2 + i)
        plt.axis('off')
        plt.imshow(X_realB[i])
    fig.suptitle(f'Model performance at {step} (src/gen/real)', fontsize=16)
    # save plot to file
    filename1 = 'plot_' + str(step) + '.png'
    plt.savefig(filename1)
    plt.show()
    plt.close()
    # save the generator model
    filename2 = 'model_' + str(step) + '.h5'
    g_model.save(filename2)
    print('>Saved: %s and %s' % (filename1, filename2))

    
def train(d_model, g_model, gan_model, d_train, d_test, n_epochs=100, n_batch=1):
    """
    train pix2pix models
    10 epochs need about 1h on Quadro T1000
    """
    
    # determine the output square shape of the discriminator
    n_patch = d_model.output_shape[1]
    # unpack dataset
    trainA, trainB = d_train
    # calculate the number of batches per training epoch
    bat_per_epo = int(len(trainA) / n_batch)
    # calculate the number of training iterations
    n_steps = bat_per_epo * n_epochs
    # manually enumerate epochs
    for i in range(n_steps):
        # select a batch of real samples
        [X_realA, X_realB], y_real = generate_real_samples(d_train, n_batch, n_patch)
        # generate a batch of fake samples
        X_fakeB, y_fake = generate_fake_samples(g_model, X_realA, n_patch)
        # update discriminator for real samples
        d_loss1 = d_model.train_on_batch([X_realA, X_realB], y_real)
        # update discriminator for generated samples
        d_loss2 = d_model.train_on_batch([X_realA, X_fakeB], y_fake)
        # update the generator
        g_loss, _, _ = gan_model.train_on_batch(X_realA, [y_real, X_realB]) #gan loss, d_model loss, g_model loss
        # summarize performance
        print(f'step{i+1}: d_loss_real[{d_loss1:.3f}] d_loss_fake[{d_loss2:.3f}] gan_loss[{g_loss:.3f}]')
        # summarize model performance
        if (i+1) % (bat_per_epo * 1) == 0:
            # every epoch
            step = "epoch" + str((i+1) / bat_per_epo)
            summarize_performance(step, g_model, d_test, n_samples=3, pic_idx=[0, 2, 9])

Loading data

Data from: http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/maps.tar.gz
Other datasets can be found here: http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/

def load_images(path, size=(256,512)):
    """
    Load all images from a directory into memory
    """
    src_list, tar_list = list(), list()
    # enumerate filenames in directory, assume all are images
    for filename in listdir(path):
        # load and resize the image
        pixels = load_img(path + filename, target_size=size)
        # convert to numpy array
        pixels = img_to_array(pixels)
        # split into satellite and map
        sat_img, map_img = pixels[:, :256], pixels[:, 256:]
        src_list.append(sat_img)
        tar_list.append(map_img)
    return [asarray(src_list), asarray(tar_list)]


def preprocess_data(data):
    # load compressed arrays
    # unpack arrays
    X1, X2 = data[0], data[1]
    # scale from [0,255] to [-1,1]
    X1 = (X1 - 127.5) / 127.5
    X2 = (X2 - 127.5) / 127.5
    return [X1, X2]

# train
path = 'maps/train/'
dataset_train = preprocess_data(load_images(path))
print('Loaded train: ', dataset_train[0].shape, dataset_train[1].shape)

# test dataset path
path = 'maps/test/'
dataset_test = preprocess_data(load_images(path))
print('Loaded train: ', dataset_test[0].shape, dataset_test[1].shape)

Loaded train:  (1096, 256, 256, 3) (1096, 256, 256, 3)
Loaded train:  (32, 256, 256, 3) (32, 256, 256, 3)

Defining and training the model

# define input shape based on the loaded dataset
image_shape =  dataset_train[0].shape[1:]

# define the models
d_model = define_discriminator(image_shape) # model
g_model = define_generator(image_shape) # model_1
gan_model = define_gan(g_model, d_model, image_shape) # model_2

start1 = datetime.now() 

train(d_model, g_model, gan_model, dataset_train, dataset_test, n_epochs=20, n_batch=1) 
#Reports parameters for each batch (total 1096) for each epoch.
#For 10 epochs we should see 10960

stop1 = datetime.now()
execution_time = stop1-start1
print("Execution time is: ", execution_time)

step1: d_loss_real[0.470] d_loss_fake[0.750] gan_loss[78.880]
step2: d_loss_real[0.336] d_loss_fake[0.632] gan_loss[70.351]
step3: d_loss_real[0.350] d_loss_fake[0.468] gan_loss[71.716]
step4: d_loss_real[0.339] d_loss_fake[0.430] gan_loss[67.774]
step5: d_loss_real[0.343] d_loss_fake[0.415] gan_loss[63.780]
step6: d_loss_real[0.329] d_loss_fake[0.387] gan_loss[54.318]
step7: d_loss_real[0.312] d_loss_fake[0.385] gan_loss[62.359]
step8: d_loss_real[0.355] d_loss_fake[0.370] gan_loss[60.752]
step9: d_loss_real[0.398] d_loss_fake[0.384] gan_loss[41.801]
step10: d_loss_real[0.298] d_loss_fake[0.354] gan_loss[51.232]
step11: d_loss_real[0.425] d_loss_fake[0.909] gan_loss[40.961]
step12: d_loss_real[0.482] d_loss_fake[0.676] gan_loss[42.712]
step13: d_loss_real[0.326] d_loss_fake[0.424] gan_loss[47.103]
step14: d_loss_real[0.253] d_loss_fake[0.335] gan_loss[44.522]
step15: d_loss_real[0.203] d_loss_fake[0.311] gan_loss[42.421]
step16: d_loss_real[0.253] d_loss_fake[0.479] gan_loss[26.772]
step17: d_loss_real[0.323] d_loss_fake[0.216] gan_loss[36.950]
step18: d_loss_real[0.407] d_loss_fake[0.258] gan_loss[25.258]
step19: d_loss_real[0.355] d_loss_fake[0.363] gan_loss[26.215]
step20: d_loss_real[0.132] d_loss_fake[0.535] gan_loss[25.699]
step21: d_loss_real[0.578] d_loss_fake[0.167] gan_loss[22.383]
step22: d_loss_real[0.446] d_loss_fake[0.246] gan_loss[29.883]
step23: d_loss_real[0.224] d_loss_fake[0.313] gan_loss[26.516]
step24: d_loss_real[0.270] d_loss_fake[0.315] gan_loss[27.072]
step25: d_loss_real[0.272] d_loss_fake[0.252] gan_loss[23.319]
step26: d_loss_real[0.210] d_loss_fake[0.286] gan_loss[21.936]
step27: d_loss_real[0.623] d_loss_fake[0.363] gan_loss[17.606]
step28: d_loss_real[0.155] d_loss_fake[0.324] gan_loss[19.935]
step29: d_loss_real[0.406] d_loss_fake[0.248] gan_loss[17.896]
step30: d_loss_real[0.219] d_loss_fake[0.312] gan_loss[17.558]
step31: d_loss_real[0.227] d_loss_fake[0.278] gan_loss[18.569]
step32: d_loss_real[0.263] d_loss_fake[0.361] gan_loss[13.865]
step33: d_loss_real[0.789] d_loss_fake[0.379] gan_loss[23.743]
step34: d_loss_real[0.380] d_loss_fake[0.268] gan_loss[17.692]
step35: d_loss_real[0.255] d_loss_fake[0.467] gan_loss[11.812]
step36: d_loss_real[0.384] d_loss_fake[0.249] gan_loss[13.465]
step37: d_loss_real[0.421] d_loss_fake[0.277] gan_loss[11.771]
step38: d_loss_real[0.375] d_loss_fake[0.501] gan_loss[12.011]
step39: d_loss_real[0.356] d_loss_fake[0.317] gan_loss[13.777]
step40: d_loss_real[0.669] d_loss_fake[0.249] gan_loss[17.766]
step41: d_loss_real[0.271] d_loss_fake[0.484] gan_loss[10.726]
step42: d_loss_real[0.392] d_loss_fake[0.313] gan_loss[11.201]
step43: d_loss_real[0.313] d_loss_fake[0.470] gan_loss[21.876]
step44: d_loss_real[0.524] d_loss_fake[0.244] gan_loss[9.634]
step45: d_loss_real[0.320] d_loss_fake[0.370] gan_loss[10.819]
step46: d_loss_real[0.349] d_loss_fake[0.355] gan_loss[9.676]
step47: d_loss_real[0.573] d_loss_fake[0.393] gan_loss[21.850]
step48: d_loss_real[0.413] d_loss_fake[0.381] gan_loss[9.491]
step49: d_loss_real[0.583] d_loss_fake[0.267] gan_loss[19.409]
step50: d_loss_real[0.435] d_loss_fake[0.423] gan_loss[12.414]
step51: d_loss_real[0.317] d_loss_fake[0.417] gan_loss[10.078]
step52: d_loss_real[0.397] d_loss_fake[0.352] gan_loss[14.124]
step53: d_loss_real[0.405] d_loss_fake[0.355] gan_loss[8.262]
step54: d_loss_real[0.374] d_loss_fake[0.311] gan_loss[13.664]
step55: d_loss_real[0.365] d_loss_fake[0.284] gan_loss[15.393]
step56: d_loss_real[0.304] d_loss_fake[0.323] gan_loss[18.366]
step57: d_loss_real[0.415] d_loss_fake[0.430] gan_loss[10.405]
step58: d_loss_real[0.465] d_loss_fake[0.234] gan_loss[26.328]
step59: d_loss_real[0.377] d_loss_fake[0.419] gan_loss[14.110]
step60: d_loss_real[0.421] d_loss_fake[0.387] gan_loss[10.137]
step61: d_loss_real[0.367] d_loss_fake[0.245] gan_loss[18.709]
step62: d_loss_real[0.461] d_loss_fake[0.355] gan_loss[14.823]
step63: d_loss_real[0.442] d_loss_fake[0.248] gan_loss[25.680]
step64: d_loss_real[0.287] d_loss_fake[0.318] gan_loss[15.695]
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before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.9992559552192688/0.9999030232429504

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch1.0.png and model_epoch1.0.h5
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before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.22329483926296234/0.9999189376831055

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch2.0.png and model_epoch2.0.h5
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before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.9760571122169495/0.9999321699142456

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch3.0.png and model_epoch3.0.h5
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before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.07764309644699097/0.9999326467514038

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch4.0.png and model_epoch4.0.h5
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before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.9528397917747498/0.9999887347221375

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch5.0.png and model_epoch5.0.h5
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before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.26454639434814453/0.9999896287918091

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch6.0.png and model_epoch6.0.h5
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before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.4699718952178955/1.0

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch7.0.png and model_epoch7.0.h5
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before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.5519761443138123/0.9999388456344604

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch8.0.png and model_epoch8.0.h5
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before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.8728542923927307/0.9999996423721313

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch9.0.png and model_epoch9.0.h5
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before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.9210769534111023/0.9999988079071045

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch10.0.png and model_epoch10.0.h5
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before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.9187984466552734/0.9999994039535522

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch11.0.png and model_epoch11.0.h5
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before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.6643887162208557/1.0

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch12.0.png and model_epoch12.0.h5
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before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.8753306865692139/1.0

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch13.0.png and model_epoch13.0.h5
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before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.8394606113433838/0.9999998211860657

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch14.0.png and model_epoch14.0.h5
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before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.6005933880805969/1.0

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch15.0.png and model_epoch15.0.h5
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before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.9514463543891907/1.0

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch16.0.png and model_epoch16.0.h5
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before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.9463770985603333/1.0

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch17.0.png and model_epoch17.0.h5
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before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.9452854990959167/1.0

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch18.0.png and model_epoch18.0.h5
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before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.9056393504142761/1.0

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch19.0.png and model_epoch19.0.h5
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step21856: d_loss_real[0.109] d_loss_fake[0.363] gan_loss[6.412]
step21857: d_loss_real[0.307] d_loss_fake[0.296] gan_loss[2.212]
step21858: d_loss_real[0.234] d_loss_fake[0.528] gan_loss[2.464]
step21859: d_loss_real[0.233] d_loss_fake[0.208] gan_loss[6.696]
step21860: d_loss_real[0.139] d_loss_fake[0.319] gan_loss[5.819]
step21861: d_loss_real[0.234] d_loss_fake[0.219] gan_loss[6.482]
step21862: d_loss_real[0.403] d_loss_fake[0.215] gan_loss[2.414]
step21863: d_loss_real[0.227] d_loss_fake[0.623] gan_loss[3.852]
step21864: d_loss_real[0.560] d_loss_fake[0.229] gan_loss[3.741]
step21865: d_loss_real[0.231] d_loss_fake[0.506] gan_loss[1.435]
step21866: d_loss_real[0.181] d_loss_fake[0.286] gan_loss[7.163]
step21867: d_loss_real[0.112] d_loss_fake[0.272] gan_loss[7.829]
step21868: d_loss_real[0.386] d_loss_fake[0.260] gan_loss[5.838]
step21869: d_loss_real[0.441] d_loss_fake[0.277] gan_loss[4.310]
step21870: d_loss_real[0.473] d_loss_fake[0.318] gan_loss[2.857]
step21871: d_loss_real[0.193] d_loss_fake[0.244] gan_loss[5.441]
step21872: d_loss_real[0.112] d_loss_fake[0.331] gan_loss[9.468]
step21873: d_loss_real[0.162] d_loss_fake[0.202] gan_loss[5.861]
step21874: d_loss_real[0.283] d_loss_fake[0.282] gan_loss[5.393]
step21875: d_loss_real[0.250] d_loss_fake[0.397] gan_loss[5.753]
step21876: d_loss_real[0.314] d_loss_fake[0.223] gan_loss[3.915]
step21877: d_loss_real[0.140] d_loss_fake[0.203] gan_loss[5.285]
step21878: d_loss_real[0.222] d_loss_fake[0.258] gan_loss[6.602]
step21879: d_loss_real[0.435] d_loss_fake[0.261] gan_loss[6.502]
step21880: d_loss_real[0.131] d_loss_fake[0.438] gan_loss[5.949]
step21881: d_loss_real[0.129] d_loss_fake[0.195] gan_loss[5.892]
step21882: d_loss_real[0.316] d_loss_fake[0.253] gan_loss[5.695]
step21883: d_loss_real[0.251] d_loss_fake[0.307] gan_loss[5.478]
step21884: d_loss_real[0.119] d_loss_fake[0.255] gan_loss[5.779]
step21885: d_loss_real[0.166] d_loss_fake[0.206] gan_loss[5.668]
step21886: d_loss_real[0.518] d_loss_fake[0.202] gan_loss[7.085]
step21887: d_loss_real[0.156] d_loss_fake[0.227] gan_loss[8.893]
step21888: d_loss_real[0.272] d_loss_fake[0.507] gan_loss[1.867]
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step21890: d_loss_real[0.310] d_loss_fake[0.313] gan_loss[7.141]
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step21892: d_loss_real[0.213] d_loss_fake[0.364] gan_loss[7.023]
step21893: d_loss_real[0.454] d_loss_fake[0.198] gan_loss[8.350]
step21894: d_loss_real[0.425] d_loss_fake[0.265] gan_loss[4.484]
step21895: d_loss_real[0.149] d_loss_fake[0.233] gan_loss[5.589]
step21896: d_loss_real[0.335] d_loss_fake[0.445] gan_loss[4.976]
step21897: d_loss_real[0.169] d_loss_fake[0.316] gan_loss[5.406]
step21898: d_loss_real[0.123] d_loss_fake[0.178] gan_loss[7.933]
step21899: d_loss_real[0.330] d_loss_fake[0.209] gan_loss[6.325]
step21900: d_loss_real[0.103] d_loss_fake[0.198] gan_loss[6.896]
step21901: d_loss_real[0.247] d_loss_fake[0.349] gan_loss[2.165]
step21902: d_loss_real[0.140] d_loss_fake[0.275] gan_loss[5.926]
step21903: d_loss_real[0.340] d_loss_fake[0.233] gan_loss[6.067]
step21904: d_loss_real[0.284] d_loss_fake[0.281] gan_loss[4.994]
step21905: d_loss_real[0.111] d_loss_fake[0.314] gan_loss[6.991]
step21906: d_loss_real[0.125] d_loss_fake[0.244] gan_loss[6.329]
step21907: d_loss_real[0.284] d_loss_fake[0.268] gan_loss[6.029]
step21908: d_loss_real[0.230] d_loss_fake[0.207] gan_loss[5.439]
step21909: d_loss_real[0.177] d_loss_fake[0.320] gan_loss[5.960]
step21910: d_loss_real[0.160] d_loss_fake[0.246] gan_loss[5.460]
step21911: d_loss_real[0.220] d_loss_fake[0.373] gan_loss[5.519]
step21912: d_loss_real[0.299] d_loss_fake[0.231] gan_loss[5.494]
step21913: d_loss_real[0.159] d_loss_fake[0.187] gan_loss[4.995]
step21914: d_loss_real[0.188] d_loss_fake[0.258] gan_loss[4.191]
step21915: d_loss_real[0.127] d_loss_fake[0.390] gan_loss[6.595]
step21916: d_loss_real[0.304] d_loss_fake[0.181] gan_loss[6.640]
step21917: d_loss_real[0.290] d_loss_fake[0.351] gan_loss[5.954]
step21918: d_loss_real[0.301] d_loss_fake[0.319] gan_loss[6.662]
step21919: d_loss_real[0.227] d_loss_fake[0.229] gan_loss[6.413]
step21920: d_loss_real[0.309] d_loss_fake[0.466] gan_loss[5.743]
before
data_set max 1.0, min -1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -1.0/1.0
shape (3, 256, 256, 3); min/max -0.8557028770446777/1.0

WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.
>Saved: plot_epoch20.0.png and model_epoch20.0.h5
Execution time is:  1:58:04.921367

Loading a saved model

The trainned model after 20 epochs can be downloaded from here: https://drive.google.com/file/d/1LvZ5izBhOUAh1qDHDHUK-ZlV703kwzmi/view?usp=sharing

model = g_model

# model = load_model('model_epoch20.0.h5') # the model after 20 epochs

Test the model on few images

def plot_images(src_img, gen_img, tar_img):
    """
    plot source, generated and target images
    """
    images = vstack((src_img, gen_img, tar_img))
    images = (images + 1) / 2.0 # scale from [-1,1] to [0,1]
    titles = ['Source', 'Generated', 'Expected']
    # plot images row by row
    for i in range(len(images)):
        plt.subplot(1, 3, 1 + i)
        plt.axis('off')
        plt.imshow(images[i])
        plt.title(titles[i])
    plt.show()

[X1, X2] = dataset_test
ix = randint(0, len(X1), 1)
src_image, tar_image = X1[ix], X2[ix]

# generate image from source
gen_image = model.predict(src_image)

plt.figure(figsize=(14,8))
plot_images(src_image, gen_image, tar_image)

Discussion

Looking into the evolution of the model with training, the improvement is slow after the first 10 epochs. The training is short (only 20 epochs), but already delivered promising results. Possible and practical improvement might be to use several satellite images, taken during different season/camera angle/light direction.
pix2pix model is used to translate image from one domain to another. It was demonstrated how to train and convert satellite images to the corresponding maps images.

References

• paper: https://arxiv.org/abs/1611.07004
• online tool for demonstration: https://phillipi.github.io/pix2pix/
• data from: http://efrosgans.eecs.berkeley.edu/pix2pix/datasets/maps.tar.gz
  
• tf tutorial: https://www.tensorflow.org/tutorials/generative/pix2pix
• used code from: https://github.com/bnsreenu/python_for_microscopists/tree/master/251_satellite_image_to_maps_translation